Risk of preterm birth following late pregnancy exposure to NSAIDs or COX-2 inhibitors
Bibliographic record
Abstract
Pregnant women may take nonsteroidal antiinflammatory drugs (NSAIDs), selective cyclooxygenase (COX)-2 inhibitors, or biological agents to relieve symptoms or manage disease flares in late pregnancy. We aimed to quantify the risk of prematurity associated with late pregnancy exposure to nonselective NSAIDs, selective COX-2 inhibitors, and biological agents. Using data from Quebec Pregnancy Cohort, we performed a population-based cohort study. We included all women who were covered by the Quebec Drug Plan and had a singleton live birth between January 1, 1998 and December 31, 2009. Late pregnancy exposure was defined as having filled at least 1 prescription for nonselective NSAIDs, selective COX-2 inhibitors, or biological agents in the 3 months before delivery. Prematurity was defined as <37 weeks of gestation. Crude and adjusted odds ratios (OR) were obtained using generalized estimation equation models. Covariates included maternal autoimmune diseases, demographics, concomitant drug use, history of pregnancy complications, and other comorbidities. A total of 156,531 pregnancies met inclusion criteria and were considered for analyses. In the 3 months before delivery, 391 pregnancies were exposed to nonselective NSAIDs, 55 to COX-2 inhibitors, and 12 to biological agents. After adjustment for maternal autoimmune diseases, concomitant medication use, and other risk factors, COX-2 inhibitor use in late pregnancy was associated with a 2.46-fold increased risk of prematurity (adjusted OR, 2.46; 95% confidence interval, 1.28-4.72) compared to nonuse; only late pregnancy exposure to celecoxib was found to increase the risk (adjusted OR, 3.41; 95% confidence interval, 1.29-9.02). In conclusion, celecoxib use during late pregnancy may increase the risk of prematurity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".